In biomechanical joint-motion analyses, the continuous motion to be studied is often approximated by a sequence of finite displacements, and the Finite Helical Axis(FHA) or "screw axis" for each displacement is estimated from position measurements on a number of anatomical or artificial landmarks. When FHA parameters are directly determined from raw (noisy) displacement data, both the position and the direction of the FHA are ill-determined, in particular when the sequential displacement steps are small. This implies, that under certain conditions, the continuous pathways of joint motions cannot be adequately described. The purpose of the present experimental study is to investigate the applicability of smoothing (or filtering)techniques, in those cases where FHA parameters are ill-determined. Two different quintic-spline smoothing methods were used to analyze the motion data obtained with Roentgenstereophotogrammetry in two experiments. One concerning carpal motions in a wrist-joint specimen, and one relative to a kinematic laboratory model, in which the axis positions are a priori known. The smoothed and nonsmoothed FHA parameter errors were compared. The influences of the number of samples and the size of the sampling interval (displacement step) were investigated, as were the effects of equidistant and nonequidistant sampling conditions and noise invariance
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The performance of human-robot collaboration tasks can be improved by incorporating predictions of the human collaborator's movement intentions. These predictions allow a collaborative robot to both provide appropriate assistance and plan its own motion so it does not interfere with the human. In the specific case of human reach intent prediction, prior work has divided the task into two pieces: recognition of human activities and prediction of reach intent. In this work, we propose a joint model for simultaneous recognition of human activities and prediction of reach intent based on skeletal pose. Since future reach intent is tightly linked to the action a person is performing at present, we hypothesize that this joint model will produce better performance on the recognition and prediction tasks than past approaches. In addition, our approach incorporates a simple human kinematic model which allows us to generate features that compactly capture the reachability of objects in the environment and the motion cost to reach those objects, which we anticipate will improve performance. Experiments using the CAD-120 benchmark dataset show that both the joint modeling approach and the human kinematic features give improved F1 scores versus the previous state of the art.
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A method to study ligament-length patterns in situ with roentgenstereophotogrammetry, using strings of glued tantalum markers, was developed. The method was tested against a bone-to-bone marking method in five carpal ligaments in three specimens, whereby the hand was moved through dorsopalmar flexion and radioulnar deviation. The "glued-string" marking method was found to be superior to the bone-to-bone marking method. The length patterns obtained were found to be reproducible in the specimens and different from earlier expectations presented in the literature. The radiocapitate ligament seems to limit the displacements of the capitate in both radial and ulnar deviation, and dorsal flexion. The radiolunate ligament has the same effect for the lunate. Both the dorsal radiotriquetrum and the palmar triquetrocapitate ligaments seem to play a stabilizing role in the neutral position of the hand, whereas the radiotriquetrum ligament also has a function in palmar flexion and the triquetrocapitate ligament functions in dorsal flexion, ultimately resisting these excursions. These findings require confirmation in more extensive experiments, whereby the relationship between ligament length patterns and carpal motion axes is investigated.
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